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Prediction of treatment response in major depression: Integration of concepts
Journal of Affective Disorders 98 (2007) 215 – 225 www.elsevier.com/locate/jad
Research report
Prediction of treatment response in major depression: Integration of concepts Christoph Mulert ⁎, Georg Juckel, Michael Brunnmeier, Susanne Karch, Gregor Leicht, Roland Mergl, Hans-Jürgen Möller, Ulrich Hegerl, Oliver Pogarell Department of Psychiatry, LMU, Munich, Germany Received 24 May 2006; received in revised form 30 July 2006; accepted 31 July 2006 Available online 20 September 2006
Abstract Background: Two promising approaches have been introduced for the prediction of treatment response in major depression: one concept is based on the activity in the rostral anterior cingulate cortex (rACC). Subjects with higher metabolic rates respond better to sleep deprivation or antidepressive medication. Another approach is the investigation of the loudness dependence of the auditory evoked potential (LDAEP). Here, a high LDAEP is supposed to reflect low central serotonergic activity. We present the first study comparing both approaches in the same group of patients. Methods: Patients with major depression (n = 20) were investigated using both resting EEG and LDAEP before treatment with either citalopram or reboxetine. Results: We found significant differences between responders and non-responders in the rACC in the theta-frequency range (6.5–8 Hz, p b 0.05). In the subgroup of patients, treated with citalopram we found higher LDAEP-values in responders versus non-responders (p b 0.05) and a significant correlation between pre-treatment-LDAEP and improvement in the Hamilton score after treatment (r = 0.71, p b 0.05). Conclusions: In combining both methods a prediction whether a patient with major depression might be at risk for non-response to a standard therapy as well as a suggestion for a pharmacological approach of choice seems to be possible. © 2006 Elsevier B.V. All rights reserved. Keywords: Major depression; EEG; LDAEP; Citalopram; Reboxetine; LORETA
1. Introduction Thirty to fifty percent of the patients with major depression do not respond to the first given medication (Thase, 2003). Due to the latency of the drug effect there
⁎ Corresponding author. Department of Psychiatry Nuβbaumstraβe 7 80336 München, Germany. Tel.: +49 89 5160 3392; fax: +49 89 5160 5542. E-mail address: [email protected] (C. Mulert). 0165-0327/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2006.07.021
is a waiting period of at least 2 to 3 weeks, partly with appearance of side-effects, until non-response to medication in a single patient has to be considered. Non-response to the first medication puts an enormous amount of distress on the depressed patient and may even increase the risk of suicide. A prediction of treatment with the individual response to an antidepressive therapy would allow to avoid the mentioned disadvantages and reach a faster success in therapy. Two major approaches based on the investigation of brain function have been introduced for the prediction of
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treatment response in major depression and were independently replicated by several groups: one approach of prediction is based on the activity/metabolic rate in the rostral (pregenual) anterior cingulate cortex (Mayberg et al., 1997): Subjects with higher metabolic rates respond better to sleep deprivation (Ebert et al., 1994; Smith et al., 1999; Wu et al., 1999) as well as antidepressive medication with paroxetine (Brody et al., 1999; Saxena et al., 2003), sertraline (Buchsbaum et al., 1997) or venlafaxine (Davidson et al., 2003). These findings have been replicated with a tomographic analysis of resting EEG-data, finding increased current source density in the anterior cingulate cortex in responders to nortriptylin in the theta-frequency range (Pizzagalli et al., 2001). In a recent meta-analysis of PET studies responders across cohorts responders differed from non-responders in a network subsystem involving both limbic afferents and cortical efferents of the rostral anterior cingulate cortex, Brodmann area 25 (Seminowicz et al., 2004). Recently, deep brain stimulation in this region was demonstrated to be helpful in patients with treatmentresistant depression (Mayberg et al., 2005). It is important to notice that several subregions of the ACC exist (Allman et al., 2001; Bush et al., 2000). While dorsal parts are typically activated in tasks requiring conflict monitoring, response selection or error processing, the rostral (pregenual) anterior cingulate cortex has been described to be related to emotional behaviour and to play a major role in the pathophysiology of depression (Mayberg, 1997). Concerning neurochemical mechanisms underlying the rACC-based response prediction there is no clear relationship to a single neurotransmitter system. A relationship to the serotonergic system has been discussed against the background of response prediction to serotonergic medication (Buchsbaum et al., 1997). Serotonergic effects of sleep deprivation have also been described, but there is also evidence for an influence of the dopamine system here (Gillin et al., 2001; Wu et al., 2001). Recently, in a study investigating sleep deprivation effects in the rat brain a significant decrease of norepinephrine transporter binding was described in a number of regions including the anterior cingulate cortex while no reduced serotonin transporter binding was described in the anterior cingulate cortex but in the olfactory nucleus and the substantia nigra (Hipolide et al., 2005). The second approach for the prediction of treatment response in major depression is based on the loudness dependence of the auditory evoked potential (LDAEP) and its modulation by the serotonergic innervation.
LDAEP is a non-invasive standardized EEG measure, which assesses the increase of N1/P2 amplitude values to increasing tone loudness/sound level during auditory stimulation. While first investigations by Buchsbaum et al. were based on the hypothesis of augmenting/ reducing (Buchsbaum and Silverman, 1968), further interest for this phenomenon has emerged in psychiatric research since several lines of evidence suggested a serotonergic modulation of the cortical loudness dependence (Hegerl et al., 2001; Hegerl and Juckel, 1993). Recently, a positive correlation between the serotonin transporter availability as assessed with beta-CIT and SPECT and the cortical LDAEP was described (Pogarell et al., 2004). In addition, a significant relationship between the loudness dependence and genes involved in the serotonergic transmission has been found (Gallinat et al., 2003; Strobel et al., 2003). Direct evidence for a possible relationship between the cortical loudness dependence and the serotonergic system comes from animal studies: Juckel et al. (Juckel et al., 1999) directly manipulated the serotonergic neurons in the dorsal raphe nucleus of the brainstem in behaving cats. Activation was associated with a decrease of the cortical loudness dependence and inhibition with an increase of the loudness dependence. Interestingly, this effect was present only in the primary auditory cortex but not in the auditory association cortex. The LDAEP of the primary auditory cortex is strong, when the activity of the serotonergic system is low, and vice versa, due to the high innervation of this brain region, but not of secondary areas, by serotonergic fibers (Lewis et al., 1986). Based on scalp-ERP-data an analysis only of the activity of the primary auditory cortex can be achieved using dipole source analysis (Scherg and von Cramon, 1985) or Low Resolution Electromagnetic Tomography (LORETA) (Pascual-Marqui et al., 1994). The possible clinical value of LDAEP for prediction of treatment with serotonin-agonistic antidepressants could already be proofed in several studies. Patients with a strong LDAEP before treatment, i.e. low serotonergic activity, better responded to fluoxetine (Paige et al., 1994), fluvoxamine (Hegerl and Juckel, 1993), fenfluramine (Bruneau et al., 1989), paroxetine (Gallinat et al., 2000) or citalopram (Mulert et al., 2002) than patients with a weak LDAEP, indicating a rather high or normal serotonergic activity. While the above mentioned studies demonstrated a close relationship between high LDAEP and treatment response to serotonergic drugs, a recent study investigating 14 patients with major depression could demonstrate the corresponding finding of a low LDAEP and response to a noradrenergic drug (reboxetine) (Linka et al., 2005).
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To our knowledge both approaches have not been combined so far. Here, we present the first study comparing both approaches in the same group of depressive inpatients with major depression. This study had three main goals: The first was to replicate earlier findings of ACC- and LDAEP-based prediction of treatment response in the same sample. The second was to investigate how both approaches are related to response to a serotonergic or noradrenergic treatment strategy. The third question was whether both methods are related to each other or not and whether a combination of both might be useful for clinical decisions. 2. Methods 2.1. Subjects Twenty acutely depressed in patients with a major depression (7 males, 13 females) were recruited just after admission to the hospital (Department of Psychiatry of the Ludwig-Maximilians-University Munich), fulfilling criteria for non-reactive, non-organic or nonneurotic unipolar depression according to DSM IV and ICD-10 (F32, F33), without any further psychiatric comorbidity. Not included were patients with addiction disorders, with reduced intelligence at moderate or severe levels, with neurological or severe somatic as well as with other disorders. Furthermore, no patients are included in the study that had continuous benzodiazepine-use for more than 10 days prior to the study or severe hearing problems as measured with an audiometer. Only patients with a Hamilton score (Bagby et al., 2004) (Ham-D, 21 items) higher than 15 (mean: 28.95, S.D.: 6.54) were included in the study (for sociodemographic data and psychopathology see Table 1). Response was defined as a 50% or more decrease in the HAM-D-scores after 4 weeks treatment with either citalopram or reboxetine. Eleven subjects were treated Table 1 Depressed patients Responder
Age (years) Pre-treatment: Hamilton Depression scale CGI Post-treatment: Hamilton Depression scale CGI
Non-responder
N (male/female) 10 (4/6) Mean ± S.D. 48.8 ± 9.8
45.4 ± 10.2
29.1 ± 7.2 6.4 ± 0.6
28.8 ± 6.1 6.3 ± 0.5
8.4 ± 4.1 2.9 ± 0.3
23.4 ± 5.2 4.6 ± 0.5
10 (3/7)
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with citalopram (7 responders and 4 non-responders) and 9 subjects with reboxetine (3 responders and 6 nonresponders). The study was approved by the local ethics committee of the Ludwig-Maximilians-University of Munich and written informed consent was obtained from each subject. 2.2. Treatment The patients willing to participate were assigned to a 1week wash-out phase from any previous medication. In the morning of day 8 the neurophysiological baseline recording of resting EEG and LDAEP was performed. No patient received fluoxetine before. Afterwards the patients were randomized and assigned to one of the two treatment arms. Randomization was the information about the kind of treatment was provided for both patients with depression and the therapeutic team. All subjects were treated with the selective serotonin reuptake inhibitor citalopram with a start dose of 20 mg and a maximal dose of 60 mg/ die [mean dose ±S.D. responder: 47.15 ± 9.5 mg/die, mean dose non-responders: 55 ± 10.0 mg/die] or reboxetine with a start dose of 4 mg and a maximal dose of 12 mg/die [mean dose responder: 8.0 ± 0 mg/die, mean dose non-responders: 9.7 ± 1.5 mg/die]. Titration of the dose was done stepwise according to the clinical situation. Study related visits were done once a week. 2.3. ERP recording The EEG/ERP recording took place before treatment with either citalopram or reboxetine, in a sound attenuated and electrically shielded room adjacent to the recording apparatus (Neuroscan Synamps). Subjects were seated in a slightly reclined chair with closed eyes during the resting EEG. EEG/Evoked potentials were recorded with 33 electrodes referred to Cz (32 channels). The electrodes were positioned according to the International 10/20 system. Electrode skin impedance was usually less than 5 kΩ. Data were collected with a sampling rate of 250 Hz and an analogous bandpass filter (0.16–50 Hz). Resting EEG (eyes closed) recording was done for 5 min. The resting EEG was visually inspected for artefact detection. After artefact rejection, segments with 2048 ms (512 data points) were selected for further processing. The mean number of segments ±S.D. was 54.8 ± 25.7 for the responders and 63.7 ± 39.8 for the non-responders (t = −0.59, df = 1,18; p = 0.56). Frequency analysis and localization were done using the LORETA software package (Pascual-Marqui et al., 1999). LORETA-cross spectrum analysis was done for the following frequency bands: delta (1.5–6.0 Hz), theta (6.5–8.0 Hz, alpha1
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(8.5–10.0 Hz), alpha2 (10.5–12.0 Hz), beta1 (12.5– 18.0 Hz), beta2 (18.5–21.0 Hz) and beta3 (21.5– 30.0 Hz). 2.4. ACC-ROI Based on a number of previous studies we defined a region of interest covering the ventral, pregenual ACC including seven voxels of Brodmann area 24, 18 voxels in Brodmann area 32 and six voxels of Brodmann area 25. The total volume of this ROI was 10.98 cm3 (see Fig. 1). 2.5. PCC-ROI To control for regional specificity, we have defined a region of interest located in the posterior cingulate cortex, including six voxels of Brodmann area 23, six voxels in Brodmann area 29, 25 voxels in Brodmann area 30 and 9 voxels of Brodmann area 31, resulting in a total volume of 15.78 cm3. 2.6. LDAEP A more detailed description is given elsewhere (Gallinat et al., 2000; Mulert et al., 2002). Tones (1000 Hz, 40 ms duration with 10 ms rise time and 10 ms fall time, ISI randomized between 1800 and 2200 ms) of five intensities (60, 70, 80, 90, 100 dB sound pressure level, generated by a PC-stimulator) were presented binaurally in pseudorandomized form by headphones. In the LDAEP-experiment 200 ms pre-stimulus and 600 ms post-stimulus periods were evaluated for 100 sweeps of each intensity (altogether 500 sweeps). For artefact-suppression an amplitude criterium has been used (± 90 μV) involving all channels at any time point during the averaging period. A minimum of 40 segments free of artefacts were required. Three subjects were excluded because of a low number (b 40) of sweeps after artefact rejection. In the remaining 17 subjects there was no significant difference between the numbers of segments between responder and non-responder at any sound pressure level. 2.7. Primary auditory cortex-(PAC)-ROI Loudness dependence was described with the median slope in a region of interest based on the probability map of the primary auditory cortex according to Penhune et al. (Penhune et al., 1996). Therefore, we defined a larger number of voxels for the following LORETA analysis on the left than on the right side (seven versus five
7.0 × 7.0 × 7.0 mm voxels). This results in an included volume of 2.40 cm3 on the left side and 1.71 cm3 on the right side (see Fig. 1). The same ROI-definition was used in our earlier studies (Mulert et al., 2002). Using this ROI-definition both for EEG-data and fMRI, we could recently show a high correlation of the respective sound level dependence (Mulert et al., 2005). In the next step, the current source density values in the timeframe 60–240 ms post-stimulus of all included voxels for all different sound pressure levels were calculated with LORETA. Thereupon, for each subject mean PAC-ROI-current source density values were calculated, including all values of both hemispheres for each sound pressure level separately. 2.8. LORETA LORETA (Pascual-Marqui et al., 1994) assumes that the smoothest of all activity distributions is most plausible (“smoothness assumption”) and therefore, a particular current density distribution is found. This fundamental assumption of LORETA directly relies on the neurophysiological observation of coherent firing of neighbouring cortical neurons during stimulus processing (Gray et al., 1989; Llinas, 1988; Silva et al., 1991) and therefore can be seen as a physiologically based constraint. However, this coherent firing has been described on the level of cortical columns, which have a much smaller diameter than the voxels used in the LORETA software; the empirical basis for coherent firing in the millimetre range is not strong enough to fully accept this constraint as a physiological one, even if it might help to produce useful results. While for typical scalp-potentials, coherent firing might be observed in brain volumes as large as or even larger than LORETA-voxels, there may also be situations (e.g. at the border of functionally distinct regions) where coherent firing is seen only in the Sub-LORETA-voxel-range. The characteristic feature of the resulting solution is its relatively low spatial resolution, which is a direct consequence of the smoothness constraint. Specifically, the solution produces a “blurred-localized” image of a point source, conserving the location of maximal activity, but with a certain degree of dispersion. The version of LORETA used in the present study used the digitized Talairach atlas (Talairach and Tournoux, 1988) available as digitized MRI from the Brain Imaging Centre, Montreal Neurologic Institute, estimating the current source density (microAmperes/mm2) distribution for either single timepoints or epochs of brain electric activity on a dense grid of 2394 voxels at 7 mm spatial resolution (Pascual-Marqui et al., 1999). The solution space (the three-dimensional space where the
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Fig. 1. Regions of interest: rostral anterior cingulate cortex (including voxels of Brodmann area 24, 25 and 32) and the primary auditory cortex.
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inverse EEG problem is solved) was restricted to the gray matter and hippocampus in the Talairach atlas (anatomically based constraint). Localization with regard to spherical and realistic head geometry was done using EEG electrode coordinates reported by Towle et al. (Towle et al., 1993). LORETA has been used increasingly in the last years (Babiloni et al., 2006; Mulert et al., 2001; Pizzagalli et al., 2006) and a close relationship to simultaneously acquired fMRI has been demonstrated recently (Mulert et al., 2005; Mulert et al., 2004). 2.9. Combination of LDAEP and ACC-resting activity In order to get a combined parameter including the information of both the LDAEP- and the ACC-theta activity we have first z-transformed both data-sets and in a second step calculated the mean of the z-transformed ACC-theta and z-transformed LDAEP-values. 2.10. Statistical analyses Statistical analysis was done using the SPSS-software package (Vers. 13.0). Values were expressed by mean± standard deviation. Group differences were assessed by two-factorial univariate ANOVA (general linear model procedure) with the two factors “group of medication” and “response” and in addition t-tests for independent sample comparisons (two-sided). Due to the small sample sizes responder non-responder comparisons were done for subgroups based on medication using Mann–Whitney U tests. Effect sizes were calculated as Cohen's d for the whole group comparisons (following the t-tests) and as θ = U / mn (Newcombe, 2006) following the Mann– Whitney U tests). Two-sided Pearson's correlation coefficients were calculated for relationships between metric co-variables (age, dosages of medication) and the electrophysiological variables. Two-sided Spearman–Brown correlation coefficients were calculated for assessment of relationships between psychopathology and electrophysiological parameters. The significance level was taken as p ≤ 0.05. A p ≤ 0.10 was regarded as statistical tendency. In order to assess the differences in the correlations between changes in the Hamilton scores and the LDAEP between patients with citalopram and patients with reboxetine we have calculated Fisher's Z. 3. Results There was no significant difference between responders and non-responders concerning age or pre-treat-
ment psychopathological state as measured with HDRS or CGI (Table 1). 3.1. ACC-resting activity An univariate ANOVA with the factors “group of medication” and “response” revealed significant differences of rACC theta-activity before therapy between responders and non-responders (F = 10.7, df = 1,18; p = 0.005) to a 4-week treatment with citalopram or reboxetine with no significant main effect of the group of medication and no significant interaction between “group of medication” and “response”. Regarding different EEG-frequency bands we found significant differences between responders and non-responders in the rACC only in the theta-frequency range (6.5–8 Hz, t = 2.83, df = 1,18; p = 0.014; Cohen's d = 1.33), but not in any other frequency range (see Fig. 2). To test for regional specificity we compared thetaactivity of responders and non-responders also in the posterior cingulate cortex. Here we did not find significant differences (t = 0.75, df = 1,18; p = 0.46). In the subgroup analyses we found a significant difference between responders and non-responders only in the reboxetine group (Z = −2.1, p b 0.05, θ = 0.056) but not for the citalopram group (Z = −1.1, p = 0.32, θ = 0.29). Concerning psychopathology, we found a significant relationship between the Hamilton score-change after treatment and the pre-treatment theta activity for the whole group (Rho = 0.49, p b 0.05) but not for the subgroups based on the medication (see Fig. 3). 3.2. LDAEP An univariate ANOVA with the factors “group of medication” and “response” revealed a statistical trend for significant differences between responders and nonresponders to a 4-week treatment with citalopram or reboxetine concerning the LDAEP before therapy (F = 3.2, df = 1,15; p = b 0.1). Nevertheless, in the posthoc Mann–Whitney U tests a significant difference between responders and non-responders was found in the subgroup treated with citalopram (Z = − 2.2, p = 0.033, θ = 0.048) with higher values for the responders (see Fig. 2). In the subgroup treated with reboxetine no significant differences were detectable (Z = − 0,8; p = 0.57, θ = 0.3). Descriptively, responders to reboxetine showed mean LDAEP-values of about 50% in comparison to non-responders (Fig. 2). Psychopathology: In the subgroup of patients, treated with citalopram we found a significant positive correlation between the Hamilton-score-change after
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3.3. Correlation ACC-theta-activity and LDAEP Neither in the whole group nor in both subgroups based on medication a significant relationship between rACC-theta values and the LDAEP was detectable. 3.4. Combination of ACC-theta-activity and LDAEP The combination of ACC-theta-activity and LDAEP (mean of the z-transformed values) allowed to discriminate responders and non-responders for the whole group (t = 2.73, df = 1,15; p = 0.016; Cohen's d = 1.41). In the subgroup analyses it allowed to discriminate responders and non-responders in patients treated with citalopram (Z = − 2.4, p = 0.017, θ = 0.0) but not in patients treated with reboxetine (Z = −0.77, p = 0.57, θ = 0.3).
Fig. 2. A: Mean loudness dependence in the primary auditory cortex. 2B: Mean theta-activity in the rACC.
treatment and the pre-treatment LDAEP (Rho = 0.71, p b 0.05). In patients treated with reboxetine, there was a negative correlation between the Hamilton-scorechange after treatment and the pre-treatment LDAEP (Rho=− 0.54). This correlation, however, was not significant (see Fig. 4). Interestingly, the difference between the positive correlation of the LDAEP with the changes in the Hamiltonscores in patients treated with citalopram and the negative correlation of the LDAEP with the changes in the Hamilton-scores in patients treated with reboxetine was significant (Fisher' Z = 1.49, p = 0.017).
Fig. 3. Correlation of the improvement in the Hamilton scale after 4 weeks treatment and the rACC theta-activity for A) patients treated with citalopram B) patients treated with reboxetine.
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Fig. 4. Correlation of the improvement in the Hamilton scale after 4 weeks treatment and the LDAEP for A) patients treated with citalopram B) patients treated with reboxetine.
Psychopathology: Concerning psychopathology, we found a significant relationship between the Hamiltonscore-change after treatment and the pre-treatment combined parameter for the whole group (Rho = 0.61, p = 0.009). In the subgroup of patients, treated with citalopram we found a strong significant positive correlation between the Hamilton-score-change after treatment and the pre-treatment combined parameter (Rho = 0.89, p = 0.001). In patients treated with reboxetine, there was no correlation between the Hamiltonscore-change after treatment and the pre-treatment combined parameter (Rho = − 0.02). 4. Discussion This is the first combination of two approaches for the prediction of treatment response in major depression: based on rACC-function and based on the analysis of the LDAEP. We could replicate earlier studies demonstrating
increased activity in the rACC of responders in comparison to non-responders (Mayberg et al., 1997; Pizzagalli et al., 2001). Here we also replicated the earlier finding of a frequency specificity for the theta-range. In addition we could show that this effect was not dependent of the kind of treatment: both responders to citalopram and responders to reboxetine showed increased pre-treatment theta-activity. Concerning the LDAEP, we could only describe a statistical trend towards a significant interaction between “group of medication” and “response” in the ANOVA. It is of interest that differential results turned out in relation to the kind of medication in the post-hoc analyses. Only in the subgroup of patients treated with the serotonergic drug citalopram a high LDAEP was associated with good response. Furthermore, we found a significant difference between the correlations of Hamilton-score change after 4-week treatment and the pre-treatment LDAEP between patients treated with citalopram in comparison to patients treated with reboxetine. While not significant, in the subgroup treated with reboxetine patients with a good response showed much smaller LDAEP-values (about 50%) in comparison to patients with a bad response. These findings are in line with a number of earlier investigations (Gallinat et al., 2000; Hegerl et al., 2001; Linka et al., 2005; Mulert et al., 2002). Summarizing our results and the results reported in the literature it can be stated that both with the LDAEP and the resting activity of the rACC treatment response can be predicted. The methods pick up different neurophysiological aspects and do not correlate. Therefore it is interesting to consider whether a combination of these approaches might be potentially clinically useful. An important point is that both approaches have different limitations. Concerning ACC-resting activity, a distinction between possible response or non-response is possible. However, no suggestion for a drug treatment strategy is offered. Concerning the LDAEP-based approach it is a limitation that general non-response is not considered but only a potential non-response to a medication using the “wrong” neurochemical strategy. While a well-founded first drug of choice might significantly reduce non-response, there will be still some patients not responding to any typical first-line treatment. These patients would not be detected using LDAEP. Therefore, form a clinical point of view, it might be useful as a first step to use the ACC-based analysis in order to detect a subgroup of patients at risk of non-response to any standard first-line drug therapy. This subgroup might benefit from using an alternative therapeutical approach directly from the beginning without wasting time with a non-successful treatment
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for weeks. For these cases therapeutical approaches for treatment-resistant depression, including lithium augmentation, have been suggested (Bauer et al., 2003a,b; Bschor et al., 2001; Bschor et al., 2003). As a second step, if response basically seems likely, LDAEP could be used in order to select the most promising drug treatment strategy: A serotonergic drug if the LDAEP is pronounced, a noradrenergic drug if the LDAEP is moderate or low. Another interesting option would be to use a combined parameter, based on the information of both LDAEP and ACC-theta-activity. In our sample, this parameter showed strongest effect sizes concerning the differentiation between responders and non-responders and highest correlations with Hamilton-score changes for both the whole group and the subgroup treated with citalopram. From a methodological point it is worth mentioning that in the present study two different concepts have been combined, which have been investigated so far usually with different methods: The LDAEP has its origin and main field in the electrophysiology (Hegerl et al., 2001; Hegerl and Juckel, 1993). However, intensity dependence in the auditory system can also be investigated with functional imaging techniques like fMRI (Brechmann et al., 2002; Jancke et al., 1998). On the other hand, ACC-activity has been mainly investigated with functional imaging methods like Positron Emission tomography (PET) (Mayberg et al., 1997; Wu et al., 1999), even if an electrophysiological approach has also been demonstrated to be successful (Pizzagalli et al., 2001). Recently, in a simultaneous EEG-fMRI study it could be shown that both methods can be used in a similar way to investigate brain function, e.g. in target detection (Mulert et al., 2004) or in the investigation of sound level dependence (Mulert et al., 2005). FMRI is more precise concerning localization, but also more expensive, usually more time consuming, more stressful for some patients and therefore not always as practical as EEG/ERP in a clinical setting. This study was initiated to explore the values of two biological predictors for treatment response; in addition it is also interesting to look at possible implications of the results for our understanding of the mechanisms behind successful treatment. While the monoamine hypothesis of depression has been challenged in the last years and the idea emerged that a secondary central adaptive change rather than the primary drug effect might be the reason for the observed clinical improvement (Hindmarch, 2002), our data (e.g. the significant difference between the correlations of Hamilton-score change and the serotonin-related-LDAEP between
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patients treated with citalopram in comparison to patients treated with reboxetine) would be consistent with a specific and discriminable role of the respective monoamine system for the clinical response. Limitations: It is necessary to mention some limitations of this study. First and most important, group sizes are small, especially for the subgroup analyses. Therefore it seems necessary to confirm our results in a larger sample. Second, while assignment to treatment (citalopram or reboxetine) was randomized, the trial was performed open-label for both patients and the therapeutic team. However, LDAEP analyses and assessment of ACC-activity (pre-treatment) were performed blind for the outcome criterium response or non-response. In conclusion, in combining both methods a prediction whether a patient with major depression might be at risk for non-response to a standard therapy as well as a suggestion for a pharmacological approach of choice seems to be possible. Follow up investigations of the non-responders are necessary in order to clarify their outcome and possible successful treatment concepts. Early detection of subjects at risk for non-response to a standard therapy might be important to start early with the most promising treatment approach. Acknowledgement This study was supported by the German Ministry for Education and Research within the promotional emphasis “German Research Network on Depression” (Project 6.3). Parts of this work were prepared in the context of Michael Brunnmeier's M. D. thesis at the Faculty of Medicine, Ludwig-Maximilians-University, Munich. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j. jad.2006.07.021. References Allman, J.M., Hakeem, A., Erwin, J.M., Nimchinsky, E., Hof, P., 2001. The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Ann. N.Y. Acad. Sci. 935, 107–117. Babiloni, C., Binetti, G., Cassarino, A., et al., 2006. Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study. Hum. Brain Mapp. 27, 162–172. Bagby, R.M., Ryder, A.G., Schuller, D.R., Marshall, M.B., 2004. The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am. J. Psychiatry 161, 2163–2177. Bauer, M., Adli, M., Baethge, C., et al., 2003a. Lithium augmentation therapy in refractory depression: clinical evidence and neurobiological mechanisms. Can. J. Psychiatry 48, 440–448.
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Research report
Prediction of treatment response in major depression: Integration of concepts Christoph Mulert ⁎, Georg Juckel, Michael Brunnmeier, Susanne Karch, Gregor Leicht, Roland Mergl, Hans-Jürgen Möller, Ulrich Hegerl, Oliver Pogarell Department of Psychiatry, LMU, Munich, Germany Received 24 May 2006; received in revised form 30 July 2006; accepted 31 July 2006 Available online 20 September 2006
Abstract Background: Two promising approaches have been introduced for the prediction of treatment response in major depression: one concept is based on the activity in the rostral anterior cingulate cortex (rACC). Subjects with higher metabolic rates respond better to sleep deprivation or antidepressive medication. Another approach is the investigation of the loudness dependence of the auditory evoked potential (LDAEP). Here, a high LDAEP is supposed to reflect low central serotonergic activity. We present the first study comparing both approaches in the same group of patients. Methods: Patients with major depression (n = 20) were investigated using both resting EEG and LDAEP before treatment with either citalopram or reboxetine. Results: We found significant differences between responders and non-responders in the rACC in the theta-frequency range (6.5–8 Hz, p b 0.05). In the subgroup of patients, treated with citalopram we found higher LDAEP-values in responders versus non-responders (p b 0.05) and a significant correlation between pre-treatment-LDAEP and improvement in the Hamilton score after treatment (r = 0.71, p b 0.05). Conclusions: In combining both methods a prediction whether a patient with major depression might be at risk for non-response to a standard therapy as well as a suggestion for a pharmacological approach of choice seems to be possible. © 2006 Elsevier B.V. All rights reserved. Keywords: Major depression; EEG; LDAEP; Citalopram; Reboxetine; LORETA
1. Introduction Thirty to fifty percent of the patients with major depression do not respond to the first given medication (Thase, 2003). Due to the latency of the drug effect there
⁎ Corresponding author. Department of Psychiatry Nuβbaumstraβe 7 80336 München, Germany. Tel.: +49 89 5160 3392; fax: +49 89 5160 5542. E-mail address: [email protected] (C. Mulert). 0165-0327/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2006.07.021
is a waiting period of at least 2 to 3 weeks, partly with appearance of side-effects, until non-response to medication in a single patient has to be considered. Non-response to the first medication puts an enormous amount of distress on the depressed patient and may even increase the risk of suicide. A prediction of treatment with the individual response to an antidepressive therapy would allow to avoid the mentioned disadvantages and reach a faster success in therapy. Two major approaches based on the investigation of brain function have been introduced for the prediction of
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treatment response in major depression and were independently replicated by several groups: one approach of prediction is based on the activity/metabolic rate in the rostral (pregenual) anterior cingulate cortex (Mayberg et al., 1997): Subjects with higher metabolic rates respond better to sleep deprivation (Ebert et al., 1994; Smith et al., 1999; Wu et al., 1999) as well as antidepressive medication with paroxetine (Brody et al., 1999; Saxena et al., 2003), sertraline (Buchsbaum et al., 1997) or venlafaxine (Davidson et al., 2003). These findings have been replicated with a tomographic analysis of resting EEG-data, finding increased current source density in the anterior cingulate cortex in responders to nortriptylin in the theta-frequency range (Pizzagalli et al., 2001). In a recent meta-analysis of PET studies responders across cohorts responders differed from non-responders in a network subsystem involving both limbic afferents and cortical efferents of the rostral anterior cingulate cortex, Brodmann area 25 (Seminowicz et al., 2004). Recently, deep brain stimulation in this region was demonstrated to be helpful in patients with treatmentresistant depression (Mayberg et al., 2005). It is important to notice that several subregions of the ACC exist (Allman et al., 2001; Bush et al., 2000). While dorsal parts are typically activated in tasks requiring conflict monitoring, response selection or error processing, the rostral (pregenual) anterior cingulate cortex has been described to be related to emotional behaviour and to play a major role in the pathophysiology of depression (Mayberg, 1997). Concerning neurochemical mechanisms underlying the rACC-based response prediction there is no clear relationship to a single neurotransmitter system. A relationship to the serotonergic system has been discussed against the background of response prediction to serotonergic medication (Buchsbaum et al., 1997). Serotonergic effects of sleep deprivation have also been described, but there is also evidence for an influence of the dopamine system here (Gillin et al., 2001; Wu et al., 2001). Recently, in a study investigating sleep deprivation effects in the rat brain a significant decrease of norepinephrine transporter binding was described in a number of regions including the anterior cingulate cortex while no reduced serotonin transporter binding was described in the anterior cingulate cortex but in the olfactory nucleus and the substantia nigra (Hipolide et al., 2005). The second approach for the prediction of treatment response in major depression is based on the loudness dependence of the auditory evoked potential (LDAEP) and its modulation by the serotonergic innervation.
LDAEP is a non-invasive standardized EEG measure, which assesses the increase of N1/P2 amplitude values to increasing tone loudness/sound level during auditory stimulation. While first investigations by Buchsbaum et al. were based on the hypothesis of augmenting/ reducing (Buchsbaum and Silverman, 1968), further interest for this phenomenon has emerged in psychiatric research since several lines of evidence suggested a serotonergic modulation of the cortical loudness dependence (Hegerl et al., 2001; Hegerl and Juckel, 1993). Recently, a positive correlation between the serotonin transporter availability as assessed with beta-CIT and SPECT and the cortical LDAEP was described (Pogarell et al., 2004). In addition, a significant relationship between the loudness dependence and genes involved in the serotonergic transmission has been found (Gallinat et al., 2003; Strobel et al., 2003). Direct evidence for a possible relationship between the cortical loudness dependence and the serotonergic system comes from animal studies: Juckel et al. (Juckel et al., 1999) directly manipulated the serotonergic neurons in the dorsal raphe nucleus of the brainstem in behaving cats. Activation was associated with a decrease of the cortical loudness dependence and inhibition with an increase of the loudness dependence. Interestingly, this effect was present only in the primary auditory cortex but not in the auditory association cortex. The LDAEP of the primary auditory cortex is strong, when the activity of the serotonergic system is low, and vice versa, due to the high innervation of this brain region, but not of secondary areas, by serotonergic fibers (Lewis et al., 1986). Based on scalp-ERP-data an analysis only of the activity of the primary auditory cortex can be achieved using dipole source analysis (Scherg and von Cramon, 1985) or Low Resolution Electromagnetic Tomography (LORETA) (Pascual-Marqui et al., 1994). The possible clinical value of LDAEP for prediction of treatment with serotonin-agonistic antidepressants could already be proofed in several studies. Patients with a strong LDAEP before treatment, i.e. low serotonergic activity, better responded to fluoxetine (Paige et al., 1994), fluvoxamine (Hegerl and Juckel, 1993), fenfluramine (Bruneau et al., 1989), paroxetine (Gallinat et al., 2000) or citalopram (Mulert et al., 2002) than patients with a weak LDAEP, indicating a rather high or normal serotonergic activity. While the above mentioned studies demonstrated a close relationship between high LDAEP and treatment response to serotonergic drugs, a recent study investigating 14 patients with major depression could demonstrate the corresponding finding of a low LDAEP and response to a noradrenergic drug (reboxetine) (Linka et al., 2005).
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To our knowledge both approaches have not been combined so far. Here, we present the first study comparing both approaches in the same group of depressive inpatients with major depression. This study had three main goals: The first was to replicate earlier findings of ACC- and LDAEP-based prediction of treatment response in the same sample. The second was to investigate how both approaches are related to response to a serotonergic or noradrenergic treatment strategy. The third question was whether both methods are related to each other or not and whether a combination of both might be useful for clinical decisions. 2. Methods 2.1. Subjects Twenty acutely depressed in patients with a major depression (7 males, 13 females) were recruited just after admission to the hospital (Department of Psychiatry of the Ludwig-Maximilians-University Munich), fulfilling criteria for non-reactive, non-organic or nonneurotic unipolar depression according to DSM IV and ICD-10 (F32, F33), without any further psychiatric comorbidity. Not included were patients with addiction disorders, with reduced intelligence at moderate or severe levels, with neurological or severe somatic as well as with other disorders. Furthermore, no patients are included in the study that had continuous benzodiazepine-use for more than 10 days prior to the study or severe hearing problems as measured with an audiometer. Only patients with a Hamilton score (Bagby et al., 2004) (Ham-D, 21 items) higher than 15 (mean: 28.95, S.D.: 6.54) were included in the study (for sociodemographic data and psychopathology see Table 1). Response was defined as a 50% or more decrease in the HAM-D-scores after 4 weeks treatment with either citalopram or reboxetine. Eleven subjects were treated Table 1 Depressed patients Responder
Age (years) Pre-treatment: Hamilton Depression scale CGI Post-treatment: Hamilton Depression scale CGI
Non-responder
N (male/female) 10 (4/6) Mean ± S.D. 48.8 ± 9.8
45.4 ± 10.2
29.1 ± 7.2 6.4 ± 0.6
28.8 ± 6.1 6.3 ± 0.5
8.4 ± 4.1 2.9 ± 0.3
23.4 ± 5.2 4.6 ± 0.5
10 (3/7)
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with citalopram (7 responders and 4 non-responders) and 9 subjects with reboxetine (3 responders and 6 nonresponders). The study was approved by the local ethics committee of the Ludwig-Maximilians-University of Munich and written informed consent was obtained from each subject. 2.2. Treatment The patients willing to participate were assigned to a 1week wash-out phase from any previous medication. In the morning of day 8 the neurophysiological baseline recording of resting EEG and LDAEP was performed. No patient received fluoxetine before. Afterwards the patients were randomized and assigned to one of the two treatment arms. Randomization was the information about the kind of treatment was provided for both patients with depression and the therapeutic team. All subjects were treated with the selective serotonin reuptake inhibitor citalopram with a start dose of 20 mg and a maximal dose of 60 mg/ die [mean dose ±S.D. responder: 47.15 ± 9.5 mg/die, mean dose non-responders: 55 ± 10.0 mg/die] or reboxetine with a start dose of 4 mg and a maximal dose of 12 mg/die [mean dose responder: 8.0 ± 0 mg/die, mean dose non-responders: 9.7 ± 1.5 mg/die]. Titration of the dose was done stepwise according to the clinical situation. Study related visits were done once a week. 2.3. ERP recording The EEG/ERP recording took place before treatment with either citalopram or reboxetine, in a sound attenuated and electrically shielded room adjacent to the recording apparatus (Neuroscan Synamps). Subjects were seated in a slightly reclined chair with closed eyes during the resting EEG. EEG/Evoked potentials were recorded with 33 electrodes referred to Cz (32 channels). The electrodes were positioned according to the International 10/20 system. Electrode skin impedance was usually less than 5 kΩ. Data were collected with a sampling rate of 250 Hz and an analogous bandpass filter (0.16–50 Hz). Resting EEG (eyes closed) recording was done for 5 min. The resting EEG was visually inspected for artefact detection. After artefact rejection, segments with 2048 ms (512 data points) were selected for further processing. The mean number of segments ±S.D. was 54.8 ± 25.7 for the responders and 63.7 ± 39.8 for the non-responders (t = −0.59, df = 1,18; p = 0.56). Frequency analysis and localization were done using the LORETA software package (Pascual-Marqui et al., 1999). LORETA-cross spectrum analysis was done for the following frequency bands: delta (1.5–6.0 Hz), theta (6.5–8.0 Hz, alpha1
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(8.5–10.0 Hz), alpha2 (10.5–12.0 Hz), beta1 (12.5– 18.0 Hz), beta2 (18.5–21.0 Hz) and beta3 (21.5– 30.0 Hz). 2.4. ACC-ROI Based on a number of previous studies we defined a region of interest covering the ventral, pregenual ACC including seven voxels of Brodmann area 24, 18 voxels in Brodmann area 32 and six voxels of Brodmann area 25. The total volume of this ROI was 10.98 cm3 (see Fig. 1). 2.5. PCC-ROI To control for regional specificity, we have defined a region of interest located in the posterior cingulate cortex, including six voxels of Brodmann area 23, six voxels in Brodmann area 29, 25 voxels in Brodmann area 30 and 9 voxels of Brodmann area 31, resulting in a total volume of 15.78 cm3. 2.6. LDAEP A more detailed description is given elsewhere (Gallinat et al., 2000; Mulert et al., 2002). Tones (1000 Hz, 40 ms duration with 10 ms rise time and 10 ms fall time, ISI randomized between 1800 and 2200 ms) of five intensities (60, 70, 80, 90, 100 dB sound pressure level, generated by a PC-stimulator) were presented binaurally in pseudorandomized form by headphones. In the LDAEP-experiment 200 ms pre-stimulus and 600 ms post-stimulus periods were evaluated for 100 sweeps of each intensity (altogether 500 sweeps). For artefact-suppression an amplitude criterium has been used (± 90 μV) involving all channels at any time point during the averaging period. A minimum of 40 segments free of artefacts were required. Three subjects were excluded because of a low number (b 40) of sweeps after artefact rejection. In the remaining 17 subjects there was no significant difference between the numbers of segments between responder and non-responder at any sound pressure level. 2.7. Primary auditory cortex-(PAC)-ROI Loudness dependence was described with the median slope in a region of interest based on the probability map of the primary auditory cortex according to Penhune et al. (Penhune et al., 1996). Therefore, we defined a larger number of voxels for the following LORETA analysis on the left than on the right side (seven versus five
7.0 × 7.0 × 7.0 mm voxels). This results in an included volume of 2.40 cm3 on the left side and 1.71 cm3 on the right side (see Fig. 1). The same ROI-definition was used in our earlier studies (Mulert et al., 2002). Using this ROI-definition both for EEG-data and fMRI, we could recently show a high correlation of the respective sound level dependence (Mulert et al., 2005). In the next step, the current source density values in the timeframe 60–240 ms post-stimulus of all included voxels for all different sound pressure levels were calculated with LORETA. Thereupon, for each subject mean PAC-ROI-current source density values were calculated, including all values of both hemispheres for each sound pressure level separately. 2.8. LORETA LORETA (Pascual-Marqui et al., 1994) assumes that the smoothest of all activity distributions is most plausible (“smoothness assumption”) and therefore, a particular current density distribution is found. This fundamental assumption of LORETA directly relies on the neurophysiological observation of coherent firing of neighbouring cortical neurons during stimulus processing (Gray et al., 1989; Llinas, 1988; Silva et al., 1991) and therefore can be seen as a physiologically based constraint. However, this coherent firing has been described on the level of cortical columns, which have a much smaller diameter than the voxels used in the LORETA software; the empirical basis for coherent firing in the millimetre range is not strong enough to fully accept this constraint as a physiological one, even if it might help to produce useful results. While for typical scalp-potentials, coherent firing might be observed in brain volumes as large as or even larger than LORETA-voxels, there may also be situations (e.g. at the border of functionally distinct regions) where coherent firing is seen only in the Sub-LORETA-voxel-range. The characteristic feature of the resulting solution is its relatively low spatial resolution, which is a direct consequence of the smoothness constraint. Specifically, the solution produces a “blurred-localized” image of a point source, conserving the location of maximal activity, but with a certain degree of dispersion. The version of LORETA used in the present study used the digitized Talairach atlas (Talairach and Tournoux, 1988) available as digitized MRI from the Brain Imaging Centre, Montreal Neurologic Institute, estimating the current source density (microAmperes/mm2) distribution for either single timepoints or epochs of brain electric activity on a dense grid of 2394 voxels at 7 mm spatial resolution (Pascual-Marqui et al., 1999). The solution space (the three-dimensional space where the
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Fig. 1. Regions of interest: rostral anterior cingulate cortex (including voxels of Brodmann area 24, 25 and 32) and the primary auditory cortex.
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inverse EEG problem is solved) was restricted to the gray matter and hippocampus in the Talairach atlas (anatomically based constraint). Localization with regard to spherical and realistic head geometry was done using EEG electrode coordinates reported by Towle et al. (Towle et al., 1993). LORETA has been used increasingly in the last years (Babiloni et al., 2006; Mulert et al., 2001; Pizzagalli et al., 2006) and a close relationship to simultaneously acquired fMRI has been demonstrated recently (Mulert et al., 2005; Mulert et al., 2004). 2.9. Combination of LDAEP and ACC-resting activity In order to get a combined parameter including the information of both the LDAEP- and the ACC-theta activity we have first z-transformed both data-sets and in a second step calculated the mean of the z-transformed ACC-theta and z-transformed LDAEP-values. 2.10. Statistical analyses Statistical analysis was done using the SPSS-software package (Vers. 13.0). Values were expressed by mean± standard deviation. Group differences were assessed by two-factorial univariate ANOVA (general linear model procedure) with the two factors “group of medication” and “response” and in addition t-tests for independent sample comparisons (two-sided). Due to the small sample sizes responder non-responder comparisons were done for subgroups based on medication using Mann–Whitney U tests. Effect sizes were calculated as Cohen's d for the whole group comparisons (following the t-tests) and as θ = U / mn (Newcombe, 2006) following the Mann– Whitney U tests). Two-sided Pearson's correlation coefficients were calculated for relationships between metric co-variables (age, dosages of medication) and the electrophysiological variables. Two-sided Spearman–Brown correlation coefficients were calculated for assessment of relationships between psychopathology and electrophysiological parameters. The significance level was taken as p ≤ 0.05. A p ≤ 0.10 was regarded as statistical tendency. In order to assess the differences in the correlations between changes in the Hamilton scores and the LDAEP between patients with citalopram and patients with reboxetine we have calculated Fisher's Z. 3. Results There was no significant difference between responders and non-responders concerning age or pre-treat-
ment psychopathological state as measured with HDRS or CGI (Table 1). 3.1. ACC-resting activity An univariate ANOVA with the factors “group of medication” and “response” revealed significant differences of rACC theta-activity before therapy between responders and non-responders (F = 10.7, df = 1,18; p = 0.005) to a 4-week treatment with citalopram or reboxetine with no significant main effect of the group of medication and no significant interaction between “group of medication” and “response”. Regarding different EEG-frequency bands we found significant differences between responders and non-responders in the rACC only in the theta-frequency range (6.5–8 Hz, t = 2.83, df = 1,18; p = 0.014; Cohen's d = 1.33), but not in any other frequency range (see Fig. 2). To test for regional specificity we compared thetaactivity of responders and non-responders also in the posterior cingulate cortex. Here we did not find significant differences (t = 0.75, df = 1,18; p = 0.46). In the subgroup analyses we found a significant difference between responders and non-responders only in the reboxetine group (Z = −2.1, p b 0.05, θ = 0.056) but not for the citalopram group (Z = −1.1, p = 0.32, θ = 0.29). Concerning psychopathology, we found a significant relationship between the Hamilton score-change after treatment and the pre-treatment theta activity for the whole group (Rho = 0.49, p b 0.05) but not for the subgroups based on the medication (see Fig. 3). 3.2. LDAEP An univariate ANOVA with the factors “group of medication” and “response” revealed a statistical trend for significant differences between responders and nonresponders to a 4-week treatment with citalopram or reboxetine concerning the LDAEP before therapy (F = 3.2, df = 1,15; p = b 0.1). Nevertheless, in the posthoc Mann–Whitney U tests a significant difference between responders and non-responders was found in the subgroup treated with citalopram (Z = − 2.2, p = 0.033, θ = 0.048) with higher values for the responders (see Fig. 2). In the subgroup treated with reboxetine no significant differences were detectable (Z = − 0,8; p = 0.57, θ = 0.3). Descriptively, responders to reboxetine showed mean LDAEP-values of about 50% in comparison to non-responders (Fig. 2). Psychopathology: In the subgroup of patients, treated with citalopram we found a significant positive correlation between the Hamilton-score-change after
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3.3. Correlation ACC-theta-activity and LDAEP Neither in the whole group nor in both subgroups based on medication a significant relationship between rACC-theta values and the LDAEP was detectable. 3.4. Combination of ACC-theta-activity and LDAEP The combination of ACC-theta-activity and LDAEP (mean of the z-transformed values) allowed to discriminate responders and non-responders for the whole group (t = 2.73, df = 1,15; p = 0.016; Cohen's d = 1.41). In the subgroup analyses it allowed to discriminate responders and non-responders in patients treated with citalopram (Z = − 2.4, p = 0.017, θ = 0.0) but not in patients treated with reboxetine (Z = −0.77, p = 0.57, θ = 0.3).
Fig. 2. A: Mean loudness dependence in the primary auditory cortex. 2B: Mean theta-activity in the rACC.
treatment and the pre-treatment LDAEP (Rho = 0.71, p b 0.05). In patients treated with reboxetine, there was a negative correlation between the Hamilton-scorechange after treatment and the pre-treatment LDAEP (Rho=− 0.54). This correlation, however, was not significant (see Fig. 4). Interestingly, the difference between the positive correlation of the LDAEP with the changes in the Hamiltonscores in patients treated with citalopram and the negative correlation of the LDAEP with the changes in the Hamilton-scores in patients treated with reboxetine was significant (Fisher' Z = 1.49, p = 0.017).
Fig. 3. Correlation of the improvement in the Hamilton scale after 4 weeks treatment and the rACC theta-activity for A) patients treated with citalopram B) patients treated with reboxetine.
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Fig. 4. Correlation of the improvement in the Hamilton scale after 4 weeks treatment and the LDAEP for A) patients treated with citalopram B) patients treated with reboxetine.
Psychopathology: Concerning psychopathology, we found a significant relationship between the Hamiltonscore-change after treatment and the pre-treatment combined parameter for the whole group (Rho = 0.61, p = 0.009). In the subgroup of patients, treated with citalopram we found a strong significant positive correlation between the Hamilton-score-change after treatment and the pre-treatment combined parameter (Rho = 0.89, p = 0.001). In patients treated with reboxetine, there was no correlation between the Hamiltonscore-change after treatment and the pre-treatment combined parameter (Rho = − 0.02). 4. Discussion This is the first combination of two approaches for the prediction of treatment response in major depression: based on rACC-function and based on the analysis of the LDAEP. We could replicate earlier studies demonstrating
increased activity in the rACC of responders in comparison to non-responders (Mayberg et al., 1997; Pizzagalli et al., 2001). Here we also replicated the earlier finding of a frequency specificity for the theta-range. In addition we could show that this effect was not dependent of the kind of treatment: both responders to citalopram and responders to reboxetine showed increased pre-treatment theta-activity. Concerning the LDAEP, we could only describe a statistical trend towards a significant interaction between “group of medication” and “response” in the ANOVA. It is of interest that differential results turned out in relation to the kind of medication in the post-hoc analyses. Only in the subgroup of patients treated with the serotonergic drug citalopram a high LDAEP was associated with good response. Furthermore, we found a significant difference between the correlations of Hamilton-score change after 4-week treatment and the pre-treatment LDAEP between patients treated with citalopram in comparison to patients treated with reboxetine. While not significant, in the subgroup treated with reboxetine patients with a good response showed much smaller LDAEP-values (about 50%) in comparison to patients with a bad response. These findings are in line with a number of earlier investigations (Gallinat et al., 2000; Hegerl et al., 2001; Linka et al., 2005; Mulert et al., 2002). Summarizing our results and the results reported in the literature it can be stated that both with the LDAEP and the resting activity of the rACC treatment response can be predicted. The methods pick up different neurophysiological aspects and do not correlate. Therefore it is interesting to consider whether a combination of these approaches might be potentially clinically useful. An important point is that both approaches have different limitations. Concerning ACC-resting activity, a distinction between possible response or non-response is possible. However, no suggestion for a drug treatment strategy is offered. Concerning the LDAEP-based approach it is a limitation that general non-response is not considered but only a potential non-response to a medication using the “wrong” neurochemical strategy. While a well-founded first drug of choice might significantly reduce non-response, there will be still some patients not responding to any typical first-line treatment. These patients would not be detected using LDAEP. Therefore, form a clinical point of view, it might be useful as a first step to use the ACC-based analysis in order to detect a subgroup of patients at risk of non-response to any standard first-line drug therapy. This subgroup might benefit from using an alternative therapeutical approach directly from the beginning without wasting time with a non-successful treatment
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for weeks. For these cases therapeutical approaches for treatment-resistant depression, including lithium augmentation, have been suggested (Bauer et al., 2003a,b; Bschor et al., 2001; Bschor et al., 2003). As a second step, if response basically seems likely, LDAEP could be used in order to select the most promising drug treatment strategy: A serotonergic drug if the LDAEP is pronounced, a noradrenergic drug if the LDAEP is moderate or low. Another interesting option would be to use a combined parameter, based on the information of both LDAEP and ACC-theta-activity. In our sample, this parameter showed strongest effect sizes concerning the differentiation between responders and non-responders and highest correlations with Hamilton-score changes for both the whole group and the subgroup treated with citalopram. From a methodological point it is worth mentioning that in the present study two different concepts have been combined, which have been investigated so far usually with different methods: The LDAEP has its origin and main field in the electrophysiology (Hegerl et al., 2001; Hegerl and Juckel, 1993). However, intensity dependence in the auditory system can also be investigated with functional imaging techniques like fMRI (Brechmann et al., 2002; Jancke et al., 1998). On the other hand, ACC-activity has been mainly investigated with functional imaging methods like Positron Emission tomography (PET) (Mayberg et al., 1997; Wu et al., 1999), even if an electrophysiological approach has also been demonstrated to be successful (Pizzagalli et al., 2001). Recently, in a simultaneous EEG-fMRI study it could be shown that both methods can be used in a similar way to investigate brain function, e.g. in target detection (Mulert et al., 2004) or in the investigation of sound level dependence (Mulert et al., 2005). FMRI is more precise concerning localization, but also more expensive, usually more time consuming, more stressful for some patients and therefore not always as practical as EEG/ERP in a clinical setting. This study was initiated to explore the values of two biological predictors for treatment response; in addition it is also interesting to look at possible implications of the results for our understanding of the mechanisms behind successful treatment. While the monoamine hypothesis of depression has been challenged in the last years and the idea emerged that a secondary central adaptive change rather than the primary drug effect might be the reason for the observed clinical improvement (Hindmarch, 2002), our data (e.g. the significant difference between the correlations of Hamilton-score change and the serotonin-related-LDAEP between
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patients treated with citalopram in comparison to patients treated with reboxetine) would be consistent with a specific and discriminable role of the respective monoamine system for the clinical response. Limitations: It is necessary to mention some limitations of this study. First and most important, group sizes are small, especially for the subgroup analyses. Therefore it seems necessary to confirm our results in a larger sample. Second, while assignment to treatment (citalopram or reboxetine) was randomized, the trial was performed open-label for both patients and the therapeutic team. However, LDAEP analyses and assessment of ACC-activity (pre-treatment) were performed blind for the outcome criterium response or non-response. In conclusion, in combining both methods a prediction whether a patient with major depression might be at risk for non-response to a standard therapy as well as a suggestion for a pharmacological approach of choice seems to be possible. Follow up investigations of the non-responders are necessary in order to clarify their outcome and possible successful treatment concepts. Early detection of subjects at risk for non-response to a standard therapy might be important to start early with the most promising treatment approach. Acknowledgement This study was supported by the German Ministry for Education and Research within the promotional emphasis “German Research Network on Depression” (Project 6.3). Parts of this work were prepared in the context of Michael Brunnmeier's M. D. thesis at the Faculty of Medicine, Ludwig-Maximilians-University, Munich. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j. jad.2006.07.021. References Allman, J.M., Hakeem, A., Erwin, J.M., Nimchinsky, E., Hof, P., 2001. The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Ann. N.Y. Acad. Sci. 935, 107–117. Babiloni, C., Binetti, G., Cassarino, A., et al., 2006. Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study. Hum. Brain Mapp. 27, 162–172. Bagby, R.M., Ryder, A.G., Schuller, D.R., Marshall, M.B., 2004. The Hamilton Depression Rating Scale: has the gold standard become a lead weight? Am. J. Psychiatry 161, 2163–2177. Bauer, M., Adli, M., Baethge, C., et al., 2003a. Lithium augmentation therapy in refractory depression: clinical evidence and neurobiological mechanisms. Can. J. Psychiatry 48, 440–448.
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